| Autor: |
Beatriz Ocaña-Tienda, Julián Pérez-Beteta, José D. Villanueva-García, José A. Romero-Rosales, David Molina-García, Yannick Suter, Beatriz Asenjo, David Albillo, Ana Ortiz de Mendivil, Luis A. Pérez-Romasanta, Elisabet González-Del Portillo, Manuel Llorente, Natalia Carballo, Fátima Nagib-Raya, Maria Vidal-Denis, Belén Luque, Mauricio Reyes, Estanislao Arana, Víctor M. Pérez-García |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Scientific Data, Vol 10, Iss 1, Pp 1-6 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2052-4463 |
| DOI: |
10.1038/s41597-023-02123-0 |
| Popis: |
Abstract Brain metastasis (BM) is one of the main complications of many cancers, and the most frequent malignancy of the central nervous system. Imaging studies of BMs are routinely used for diagnosis of disease, treatment planning and follow-up. Artificial Intelligence (AI) has great potential to provide automated tools to assist in the management of disease. However, AI methods require large datasets for training and validation, and to date there have been just one publicly available imaging dataset of 156 BMs. This paper publishes 637 high-resolution imaging studies of 75 patients harboring 260 BM lesions, and their respective clinical data. It also includes semi-automatic segmentations of 593 BMs, including pre- and post-treatment T1-weighted cases, and a set of morphological and radiomic features for the cases segmented. This data-sharing initiative is expected to enable research into and performance evaluation of automatic BM detection, lesion segmentation, disease status evaluation and treatment planning methods for BMs, as well as the development and validation of predictive and prognostic tools with clinical applicability. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
Full text from SpringerLink